COVID-19 Data Analysis

This project explores COVID-19 vaccination rates, infection rates, and death counts. We use descriptive statistics and data visualization to identify trends and patterns.

import pandas as pd
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns

The dataset contains COVID data from around 187 countries.

data = pd.read_csv('OneDrive/NSDCprojects/Capsule_COVID_Data_Visualization/country_wise_latest.csv')
data.head()

	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	1 week change	1 week % increase	WHO Region
0	Afghanistan	36263	1269	25198	9796	106	10	18	3.50	69.49	5.04	35526	737	2.07	Eastern Mediterranean
1	Albania	4880	144	2745	1991	117	6	63	2.95	56.25	5.25	4171	709	17.00	Europe
2	Algeria	27973	1163	18837	7973	616	8	749	4.16	67.34	6.17	23691	4282	18.07	Africa
3	Andorra	907	52	803	52	10	0	0	5.73	88.53	6.48	884	23	2.60	Europe
4	Angola	950	41	242	667	18	1	0	4.32	25.47	16.94	749	201	26.84	Africa

At first glance, we can examine whether there is a relationship between the number of confirmed cases and the number of deaths.

print(data[['Deaths','Recovered']].corr())
sns.scatterplot(x='Deaths', y='Recovered', data=data)
plt.show()

             Deaths  Recovered
Deaths     1.000000   0.832098
Recovered  0.832098   1.000000

The statistical data about the data:

data.describe()

	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	1 week change	1 week % increase
count	1.870000e+02	187.000000	1.870000e+02	1.870000e+02	187.000000	187.000000	187.000000	187.000000	187.000000	187.00	1.870000e+02	187.000000	187.000000
mean	8.813094e+04	3497.518717	5.063148e+04	3.400194e+04	1222.957219	28.957219	933.812834	3.019519	64.820535	inf	7.868248e+04	9448.459893	13.606203
std	3.833187e+05	14100.002482	1.901882e+05	2.133262e+05	5710.374790	120.037173	4197.719635	3.454302	26.287694	NaN	3.382737e+05	47491.127684	24.509838
min	1.000000e+01	0.000000	0.000000e+00	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	1.000000e+01	-47.000000	-3.840000
25%	1.114000e+03	18.500000	6.265000e+02	1.415000e+02	4.000000	0.000000	0.000000	0.945000	48.770000	1.45	1.051500e+03	49.000000	2.775000
50%	5.059000e+03	108.000000	2.815000e+03	1.600000e+03	49.000000	1.000000	22.000000	2.150000	71.320000	3.62	5.020000e+03	432.000000	6.890000
75%	4.046050e+04	734.000000	2.260600e+04	9.149000e+03	419.500000	6.000000	221.000000	3.875000	86.885000	6.44	3.708050e+04	3172.000000	16.855000
max	4.290259e+06	148011.000000	1.846641e+06	2.816444e+06	56336.000000	1076.000000	33728.000000	28.560000	100.000000	inf	3.834677e+06	455582.000000	226.320000

Next, we will choose a specific WHO Region as the focus of the first part of our data visualization.

who_region_data = data.groupby('WHO Region').sum().reset_index()

# Plot the trends for confirmed cases, deaths, and recoveries across WHO regions
fig, axes = plt.subplots(3, 1, figsize=(8,12))

sns.barplot(ax=axes[0], x='WHO Region', y='Confirmed', data=who_region_data)
axes[0].set_title('Confirmed Cases by WHO Region')
axes[0].set_ylabel('Confirmed Count Across WHO Regions')

sns.barplot(ax=axes[1], x='WHO Region', y='Deaths', data=who_region_data)
axes[1].set_title('Deaths by WHO Region')
axes[1].set_ylabel('Death Count Across WHO Regions')

sns.barplot(ax=axes[2], x='WHO Region', y='Recovered', data=who_region_data)
axes[2].set_title('Recoveries by WHO Region')
axes[2].set_ylabel('Recovered Count Across WHO Regions')

plt.tight_layout()
plt.show()

Because the Amercias had the most confirmed, deaths, and recovery cases among all WHO Regions, we will first focus our analysis on cases in the Americas.

Data Analysis of Cases in the Americas

d_america = data[data['WHO Region'] == 'Americas'].sort_values(by='Confirmed', ascending = False)
new_data = d_america.head(10) #top 10 countries
new_data

	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	1 week change	1 week % increase	WHO Region
173	US	4290259	148011	1325804	2816444	56336	1076	27941	3.45	30.90	11.16	3834677	455582	11.88	Americas
23	Brazil	2442375	87618	1846641	508116	23284	614	33728	3.59	75.61	4.74	2118646	323729	15.28	Americas
111	Mexico	395489	44022	303810	47657	4973	342	8588	11.13	76.82	14.49	349396	46093	13.19	Americas
132	Peru	389717	18418	272547	98752	13756	575	4697	4.73	69.93	6.76	357681	32036	8.96	Americas
35	Chile	347923	9187	319954	18782	2133	75	1859	2.64	91.96	2.87	333029	14894	4.47	Americas
37	Colombia	257101	8777	131161	117163	16306	508	11494	3.41	51.02	6.69	204005	53096	26.03	Americas
6	Argentina	167416	3059	72575	91782	4890	120	2057	1.83	43.35	4.21	130774	36642	28.02	Americas
32	Canada	116458	8944	0	107514	682	11	0	7.68	0.00	inf	112925	3533	3.13	Americas
51	Ecuador	81161	5532	34896	40733	467	17	0	6.82	43.00	15.85	74620	6541	8.77	Americas
20	Bolivia	71181	2647	21478	47056	1752	64	309	3.72	30.17	12.32	60991	10190	16.71	Americas

new_data.describe()

	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	1 week change	1 week % increase
count	1.000000e+01	10.000000	1.000000e+01	1.000000e+01	10.000000	10.000000	10.000000	10.000000	10.000000	10.0000	1.000000e+01	10.000000	10.000000
mean	8.559080e+05	33621.500000	4.328866e+05	3.893999e+05	12457.900000	340.200000	9067.300000	4.900000	51.276000	inf	7.576744e+05	98233.600000	13.644000
std	1.398190e+06	48126.771919	6.313155e+05	8.643672e+05	17243.211981	350.807893	12164.741273	2.832749	27.626579	NaN	1.242506e+06	157603.809848	8.272305
min	7.118100e+04	2647.000000	0.000000e+00	1.878200e+04	467.000000	11.000000	0.000000	1.830000	0.000000	2.8700	6.099100e+04	3533.000000	3.130000
25%	1.291975e+05	6343.250000	4.431575e+04	4.720625e+04	1847.250000	66.750000	696.500000	3.420000	33.925000	5.2275	1.173872e+05	11366.000000	8.817500
50%	3.025120e+05	9065.500000	2.018540e+05	9.526700e+04	4931.500000	231.000000	3377.000000	3.655000	47.185000	8.9600	2.685170e+05	34339.000000	12.535000
75%	3.940460e+05	37621.000000	3.159180e+05	1.147508e+05	15668.500000	558.250000	10767.500000	6.297500	74.190000	13.9475	3.556098e+05	51345.250000	16.352500
max	4.290259e+06	148011.000000	1.846641e+06	2.816444e+06	56336.000000	1076.000000	33728.000000	11.130000	91.960000	inf	3.834677e+06	455582.000000	28.020000

The first plot is a bar plot describing the active cases among countries in the Americas.

active = new_data[['Country/Region','Active']].sort_values(by='Active',ascending=False)
sns.barplot(y=active.get('Country/Region'),x=active.get('Active'))

<Axes: xlabel='Active', ylabel='Country/Region'>

Insights:

• For countries in the Americas, the US had far more active counts than any other countries, with almost five times more active counts than Brazil, the country with the second most active counts. This strongly suggests that the US suffered the most from COVID-19 within the Americas.

The second chart is a double bar chart to compare Confirmed and Recovered COVID Cases in different countries

plt.figure(figsize=(10, 5))
X_axis = np.arange(len(new_data['Confirmed']))
plt.bar(X_axis - 0.2,new_data['Confirmed'] , 0.4, label = 'Confirmed')
plt.bar(X_axis + 0.2, new_data['Recovered'], 0.4, label = 'Recovered')

plt.xticks(X_axis,new_data['Country/Region'] )
plt.xlabel("Confirmed")
plt.ylabel("Recovered")
plt.title("Comparing the Confirmed and Recovered Counts in the Americas")

plt.legend()
plt.show()

Insights:

• For countries in the Americas, the US and Brazil has the most confirmed and recovered patients.
• While the US had more confirmed counts, Brazil had more Recovered patients, suggesting Brazil had better control over the pandemic than the US.
• Smaller countries like Mexico and Chile had about the same amount of confirmed and recovered patients, which means these countries had effective control over the pandemic.

The third is a pie chart describing the recovered cases.

plt.figure(figsize=(8,8))
patches, text, autotexts = plt.pie(new_data['Recovered'], labels = new_data['Country/Region'],autopct="%0.2f%%", pctdistance=0.8)
plt.title("Distribution of Recovered COVID Cases") #Hint: Check the TODO statement!
plt.axis('equal')
plt.legend(patches,new_data['Country/Region'] )
plt.show()

Insights:

• Brazil had the most amount of recovered COVID cases, suggesting its effective control over the pandemic.
• Canada had no recovered COVID cases, suggesting the need for improvements in its control strategies.

Next, we are going to compare Active cases in the Americas using a donut chart.

x = new_data['Active'].to_list()
labels = new_data['Country/Region']
colors = ['#0F52BA','#4169E1', '#0096FF',
          '#87CEEB','#89CFF0', '#7DF9FF']
explode = (0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05)

plt.pie(x, colors=colors, labels=labels,
        autopct='%1.1f%%', pctdistance=0.85,
        explode=explode)

centre_circle = plt.Circle((0, 0), 0.65, fc='white')
fig = plt.gcf()

fig.gca().add_artist(centre_circle)

plt.title('Active Cases in the Americas')
plt.show()

Insights:

• The US had the highest number of active cases in the Americas, accounting for over 70% of all active cases in the region. This indicates that the COVID situation in the US was the most severe in the Americas.

We will then visualize the amount of confirmed cases in the Americas through a heatmap with Choropleth.

fig = px.choropleth(new_data,
                    locations= 'Country/Region',
                    locationmode='country names',
                    color='Confirmed',
                    color_continuous_scale='Reds',
                    hover_name='Country/Region',
                    title='Total Confirmed Cases in the Americas')

fig.show()

Insights:

• North America generally had more confirmed COVID cases than South Anmerica.
• Although the US had the most serious pandemic, Canada wasn't affected really seriously.

Finally, in order to see the influence of the pandemic, we will calculate mortality and recovery rates, aggregate the data by WHO region, and then plot these rates using a barplot.

data['Mortality Rate'] = (data['Deaths'] / data['Confirmed']) * 100
data['Recovery Rate'] = (data['Recovered'] / data['Confirmed']) * 100

# Aggregate data by WHO Region
numeric_columns = ['Confirmed', 'Deaths', 'Recovered', 'Active', 'Mortality Rate', 'Recovery Rate']
who_region_rates = data.groupby('WHO Region')[numeric_columns].mean().reset_index()

fig, axes = plt.subplots(2, 1, figsize=(8, 8))

sns.barplot(ax=axes[0], x='WHO Region', y='Mortality Rate', data=who_region_rates)
axes[0].set_title('Mortality Rate by WHO Region')
axes[0].set_ylabel('Mortality Rate (%)')

sns.barplot(ax=axes[1], x='WHO Region', y='Mortality Rate', data=who_region_rates)
axes[1].set_title('Mortality Rate by WHO Regions')
axes[1].set_ylabel('Mortality Rate)')

plt.tight_layout()
plt.show()

Insights:

• Europe had the most mortality rate, suggesting the most serious pandemic conditions in Europe.
• South-East Asia and Western Pacific had the least mortality rate, suggesting their effective control during COVID-19.

Data Analysis for Cases in Southeasst Asia

Southeast Asia was chosen as the focus region because it is geographically close to China, where COVID-19 was first reported. Due to this proximity, countries in Southeast Asia were exposed to the virus relatively early, making the region important for understanding how the outbreak spread and how different countries were affected.

asia_data = data[data.get('WHO Region') == 'South-East Asia']
asia_data

	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	1 week change	1 week % increase	WHO Region	Mortality Rate	Recovery Rate
13	Bangladesh	226225	2965	125683	97577	2772	37	1801	1.31	55.56	2.36	207453	18772	9.05	South-East Asia	1.310642	55.556636
19	Bhutan	99	0	86	13	4	0	1	0.00	86.87	0.00	90	9	10.00	South-East Asia	0.000000	86.868687
27	Burma	350	6	292	52	0	0	2	1.71	83.43	2.05	341	9	2.64	South-East Asia	1.714286	83.428571
79	India	1480073	33408	951166	495499	44457	637	33598	2.26	64.26	3.51	1155338	324735	28.11	South-East Asia	2.257186	64.264803
80	Indonesia	100303	4838	58173	37292	1525	57	1518	4.82	58.00	8.32	88214	12089	13.70	South-East Asia	4.823385	57.997268
106	Maldives	3369	15	2547	807	67	0	19	0.45	75.60	0.59	2999	370	12.34	South-East Asia	0.445236	75.601069
119	Nepal	18752	48	13754	4950	139	3	626	0.26	73.35	0.35	17844	908	5.09	South-East Asia	0.255973	73.346843
158	Sri Lanka	2805	11	2121	673	23	0	15	0.39	75.61	0.52	2730	75	2.75	South-East Asia	0.392157	75.614973
167	Thailand	3297	58	3111	128	6	0	2	1.76	94.36	1.86	3250	47	1.45	South-East Asia	1.759175	94.358508
168	Timor-Leste	24	0	0	24	0	0	0	0.00	0.00	0.00	24	0	0.00	South-East Asia	0.000000	0.000000

The following scatter plot describes the relationship between the 1 week % increase and the recovery rate. The color of each point represents the amount of confirmed cases. The larger the point, the more confirmed cases.

plt.scatter(asia_data.get('1 week % increase'), asia_data.get('Recovery Rate'), c = asia_data.get('Confirmed'), cmap = 'cividis')
plt.xlabel('1 Week % Increase')
plt.ylabel('Recovery Rate')
plt.title('Recovery Rate vs. 1 Week % Increase')

for i in range(len(asia_data)):
    x = asia_data.get('1 week % increase').iloc[i]
    y = asia_data.get('Recovery Rate').iloc[i]
    label = asia_data.get('Country/Region').iloc[i]

    plt.annotate(
        label,
        (x, y),
        textcoords="offset points",
        xytext=(5, 5),
        ha='center',
        fontsize=8
    )

plt.show()

Insights:

• Overall, there is no strong linear relationship between the two variables. Countries with a low weekly increase can still have either high or low recovery rates. For example, Thailand, Bhutan, and Sri Lanka have low 1-week increases but relatively high recovery rates, suggesting that slower case growth may be associated with better recovery outcomes in some countries.
• On the other hand, India stands out as an outlier. It has the highest 1-week percentage increase, while its recovery rate is only moderate compared to other countries. This may indicate that rapid case growth can put pressure on healthcare systems, slowing recovery.
• Timor-Leste is another notable outlier, with both a very low increase and an extremely low recovery rate. This could reflect limited healthcare capacity or delays in reporting recoveries.

The second Data Visualization of data in Southeast Asia is a heatmap showcasing the mortality rate.

fig = px.choropleth(
    asia_data,
    locations='Country/Region',
    locationmode='country names',
    color='Mortality Rate',
    hover_name='Country/Region',
    color_continuous_scale='Blues',
    title='COVID-19 Mortality Rate in Asia'
)

fig.show()

Insights:

• From the heatmap, countries in Southeast Asia show noticeable variation in mortality rates. Some countries are shaded darker blue, indicating higher mortality rates, while others remain much lighter. This suggests that even within the same region, outcomes differed significantly.
• Also, countries closer to China showed a lighter color, which means that geographic proximity alone does not fully determine mortality rate. Factors such as healthcare capacity, population density, government response, and reporting practices likely played a role in these differences.

The third visualization on the Southeast Asia region is a pie chart describing the amount of active cases in the top five countries.

top5 = asia_data.sort_values('Active', ascending=False).head(5)
others = asia_data['Active'].sum() - top5['Active'].sum()

labels = list(top5['Country/Region']) + ['Others']
sizes = list(top5['Active']) + [others]

plt.figure(figsize=(10, 10))

wedges, texts, autotexts = plt.pie(
    sizes,
    labels=labels,
    autopct='%1.1f%%',
    startangle=140,
    colors = plt.cm.YlOrBr(np.linspace(0.4, 1, len(sizes))),
    wedgeprops={'edgecolor': 'white', 'linewidth': 1},
    pctdistance=0.5
)

for text in texts:
    text.set_fontsize(10)
for text in autotexts:
    text.set_fontsize(10)
    text.set_fontweight('bold')

plt.title('Distribution of Active COVID-19 Cases in Asia (Top 5 Countries)')
plt.axis('equal')

plt.show()

Insights:

• A small number of countries account for a large proportion of active cases, while the remaining countries contribute a much smaller share.
• While according to the pie chart, India had the most active cases, it's mortality rate wasn't the highest, suggesting its effective control over the pandemic.